Doppler and inertial navigation technologies have been in use in various forms since at least the 1950s when critical advances in inertial measurement and Doppler radar were achieved. These were applied successfully in aircraft navigation systems combining inertial measurement units (IMU) with onboard radar that enabled calibration of inertial drift based on observed ground speed. These systems were collectively known as Doppler-Inertial Navigation Data Systems and are well taught in technical literature including U.S. Pat. No. 2,914,763 (Greenwood and Berger, 1959) and U.S. Pat. No. 3,028,592 (Par, et. al., 1962).
The basic principle behind these systems is to combine the inherent long-term measurement accuracy of the Doppler velocity in concert with the short term precision and resolution provided by inertial systems. Inertial systems can produce high resolution, and relative high precision observables at high data rates, but will tend to drift in terms of accuracy over long intervals. Doppler observables in contrast have lesser resolution and precision at slower data rates but do not suffer drift over long intervals. Using a filter (a servo controller in those days), these observables were combined to produce a corrected velocity data that provided high data rates with high resolution and minimal drift over long intervals. Doppler velocity observables were based upon reflection of radar signals from a nominally static, earth-fixed surface. To make these systems function required both a radio frequency transmitter and receiver aboard the vehicle. These systems were used in aircraft navigation until supplanted by more advanced positioning and navigation technologies such as LORAN and GPS, which had the benefit of requiring only a radio frequency receiver—a receive-only architecture.
Similar Doppler-Inertial techniques were applied using acoustics for the autonomous navigation of an underwater vehicle. In this configuration the acoustic transceivers (e.g. sonar) provided Doppler velocity estimation that further constrained the IMU, accomplishing the same effect as the aircraft-based Doppler-Inertial Navigation Data Systems. This technique is described and taught in various technical references including the work published by Hegrenás, and Berglund (2009).
The advent of Global Navigation Satellite Systems (GNSS), including the U.S. Global Positioning System (GPS), advanced the techniques of hybrid inertial navigation, where precise GPS ranging and Doppler observables were used to provide continual calibration of IMU data enabling a navigation solution that could simultaneously provide high update rate (in some cases in the kilohertz) while maintaining high accuracy over long periods of times. These systems also addressed some of the weaknesses in radio navigation systems providing data even during intermittent outages due to obstructions (e.g., during aircraft maneuvers). These GNSS-Inertial Navigation Systems (GNSS/INS) hybrids have been used extensively in military, space, and commercial guidance and navigation applications. The principles of both loosely-coupled and tightly-coupled GNSS/INS systems have been well taught and covered in research papers and patents for example U.S. Pat. No. 5,416,712 (Geier, et. al., 1995) and U.S. Pat. No. 6,900,760 (Groves, 2005).
Loosely-coupled GNSS/INS is similar to the original Doppler-Inertial Navigation Data Systems where the processed ranging observables and inertial observations are combined by a filter maximizing data rate, precision, and long-term accuracy. These systems perform well during short term outages of GNSS and in situations where the device is undergoing only moderate dynamics. These systems are commonly used in vehicle land navigation, marine navigation, and general and commercial aircraft systems.
A GNSS receiver (e.g. a GPS receiver) produces position and velocity information after acquiring signals from at least four or more satellites as well as a valid set of precision orbit elements of the satellites. If the vertical height is known a priori or constrained, a minimum of three satellites are required to produce position and velocity estimates. An IMU produces acceleration observables (linear and rotational). Both sets of observables are typically used to update an Extended Kalman Filter (EKF), which produces the fused set of observations comprising near real-time acceleration, velocity, and position in three dimensions.
Tightly-coupled GNSS/INS is significantly more complicated than the loosely-coupled approach in that IMU data is integrated into the RF signal processor tracking loops used to track the GNSS satellites. In applications with very high dynamics (e.g. metric launch or missile navigation), tightly-coupled GNSS/INS makes it possible for the navigation system to operate. The IMU observables provide the means to do active rate aiding of the tracking loops so that the tracking loop can maintain lock when the frequency rate of change is greater than the tracking filter bandwidth.
For land, sea, and air vehicles, these combined Doppler/inertial techniques, whether using GNSS, radar, or acoustic transponders combined with IMU, have proven effective in creating robust and highly accurate navigation information. Yet when considering modern mobile computing devices including smart phones, tablets, and tracking devices that must operate in highly complex, often GNSS obstructed environments; direct application of these techniques has limited feasibility.
Mobile device autonomous positioning and navigation in indoor environments using combined Doppler-inertial techniques must deal with the challenges of multipath contamination, Rayleigh fading, and potential RF/acoustic interference from other active emitting devices. Current Doppler-inertial techniques as taught by the previous art are insufficient given the impracticality of using device-based active RF or acoustic emitters, which would decrease battery life, increase ambient RF noise and be susceptible to interference from other devices in the vicinity. While possible to deploy purpose-built infrastructure to support current Doppler-inertial techniques it inherently weakens the advantages since it is antithetic to the concept of autonomy.
Work by others to combine MEMS inertial technology, Assisted GPS (AGPS) technology, and/or WLAN positioning techniques has demonstrated some success, but has yet to achieve the performance needed to provide reliable and accurate positioning and navigation data over long intervals of distance and time. Work exemplified by Lachapelle (2004), Seitz et. al. (2007), and Renaudin et. al. (2007) teach various methods and techniques for combining these systems to provide pedestrian navigation in GNSS obstructed environments.
One successful approach to indoor pedestrian navigation was demonstrated by Foxlin (2005), where a shoe mounted MEMS IMU was capable of producing accurate navigation over long intervals using a technique of zero velocity update (ZUPT) to correct the inherent drift in the IMU. Jimenez (2010) further developed these techniques to improve performance combined with GPS data as well. These techniques rely on the frequent correction provided by the ZUPT performed at each step when the pedestrian foot touches the ground and is momentarily without motion. The interest in this technique continues as it has been shown to provide very high performance even in the most complex environments. Yet it has one significant drawback, it requires the placement of the IMU sensor on the foot in order to achieve the most reliable and effective ZUPT correction. When applied to an IMU sensor in a mobile device such as a smart phone, the additional degrees of freedom due to the device being held in the hand results in the ZUPT correction being much less effective since the motion of the hand can obscure the actual step. In an attempt to correct for this condition, the use of map matching has been tried with some limited success.
Accordingly, for navigation using mobile devices in heavily GNSS obstructed environments, there is a need for a better solution to produce autonomous positioning and velocity information that can take advantage of the high resolution and precision of MEMS IMU sensors while maintaining long-term accuracy. The use of Doppler aiding for inertial navigation in these mobile devices is a natural adaptation of the previous embodiments. However, the methods and systems used previously to provide these capabilities are impractical for mobile devices in complex multipath environments. Novel methods and systems are required in order to meet the challenges of the complex operating environments and device constraints: methods and systems that can exploit readily available signals in a complex multipath environment with minimal impact on the devices size, weight, and power requirements. Such is the nature of the present invention.